Lianantonakis, et al. (2007) [1]}. This method uses Haralick features [2]} derived from the gray-level co-occurrence matrix (GLCM) and couples this with active contours to arrive at a binary class mapping.
We extend this work to multiple classes by simply using the same feature descriptors as the original work but apply \(k\) -means++ to cluster; a similar replication approach is used in [3]}. We ran \(k\) -means++ with 100 random initializations and selected the run that produces the minimum within cluster sum of squares error in a manner consistent with [4]}.
